import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torchinfo import summary


# -----------------------input size>=32---------------------------------
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152']


model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}


def conv3x1(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv1d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv1d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x1(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm1d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x1(planes, planes)
        self.bn2 = nn.BatchNorm1d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = conv1x1(inplanes, planes)
        self.bn1 = nn.BatchNorm1d(planes)
        self.conv2 = conv3x1(planes, planes, stride)
        self.bn2 = nn.BatchNorm1d(planes)
        self.conv3 = conv1x1(planes, planes * self.expansion)
        self.bn3 = nn.BatchNorm1d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, in_channel=1, out_channel=10, zero_init_residual=False, widen_factor=1.0,
                 block_inplanes=None):
        super(ResNet, self).__init__()
        if block_inplanes is None:
            block_inplanes = [64, 128, 256, 512, 128]
        block_inplanes = [int(x * widen_factor) for x in block_inplanes]

        self.inplanes = block_inplanes[0]
        self.conv1 = nn.Conv1d(in_channel, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm1d(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, block_inplanes[0], layers[0])

        last_block_inplanes = block_inplanes[0]
        self.bypass = 0

        if layers[1] > 0:
            self.layer2 = self._make_layer(block, block_inplanes[1], layers[1], stride=2)
            last_block_inplanes = block_inplanes[1]
            self.bypass = 1
        if layers[2] > 0:
            self.layer3 = self._make_layer(block, block_inplanes[2], layers[2], stride=2)
            last_block_inplanes = block_inplanes[2]
            self.bypass = 2
        if layers[3] > 0:
            self.layer4 = self._make_layer(block, block_inplanes[3], layers[3], stride=2)
            last_block_inplanes = block_inplanes[3]
            self.bypass = 3

        self.avgpool = nn.AdaptiveAvgPool1d(1)
        # self.layer5 = nn.Sequential(
        #     nn.Linear(block_inplanes[3] * block.expansion, block_inplanes[4]),
        #     nn.ReLU(inplace=True),
        #     nn.Dropout(),
        # )
        self.fc = nn.Linear(last_block_inplanes * block.expansion, out_channel)


        for m in self.modules():
            if isinstance(m, nn.Conv1d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                nn.BatchNorm1d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)

        if self.bypass > 0:
            x = self.layer2(x)
        if self.bypass > 1:
            x = self.layer3(x)
        if self.bypass > 2:
            x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        # x = self.layer5(x)
        x = self.fc(x)

        return x


def resnet18(pretrained=False, **kwargs):
    """Constructs a ResNet-18 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
    return model


def resnet34(pretrained=False, **kwargs):
    """Constructs a ResNet-34 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
    return model


def resnet50(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
    return model


def resnet101(pretrained=False, **kwargs):
    """Constructs a ResNet-101 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
    return model


def resnet152(pretrained=False, **kwargs):
    """Constructs a ResNet-152 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
    return model


if __name__ == '__main__':
    model = ResNet(BasicBlock, [1, 0, 0, 0], in_channel=1, out_channel=1, widen_factor=1/4,
                   block_inplanes=[64, 128, 256, 512, 128])
    summary(model, (10, 1, 512))
